A transcriptomic and epigenomic cell atlas of the mouse primary motor cortex
Open accessPublished version of a preprint
Abstract
Single-cell transcriptomics can provide quantitative molecular signatures for large, unbiased samples of the diverse cell types in the brain 1–3 . With the proliferation of multi-omics datasets, a major challenge is to validate and integrate results into a biological understanding of cell-type organization. Here we generated transcriptomes and epigenomes from more than 500,000 individual cells in the mouse primary motor cortex, a structure that has an evolutionarily conserved role in locomotion. We developed computational and statistical methods to integrate multimodal data and quantitatively validate cell-type reproducibility. The resulting reference atlas—containing over 56 neuronal cell types that are highly replicable across analysis methods, sequencing technologies and modalities—is a comprehensive molecular and genomic account of the diverse neuronal and non-neuronal cell types in the mouse primary motor cortex. The atlas includes a population of excitatory neurons that resemble pyramidal cells in layer 4 in other cortical regions 4 . We further discovered thousands of concordant marker genes and gene regulatory elements for these cell types. Our results highlight the complex molecular regulation of cell types in the brain and will directly enable the design of reagents to target specific cell types in the mouse primary motor cortex for functional analysis.
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
The NP-CT-L6b-like subclasses emerge as a group highly distinct from the IT-ET-like subclasses, forming two distinct classes, IT-ET Glut and NP-CT-L6b Glut.
“We also observed that the NP–CT–L6b-like (NP, near-projecting; CT, corticothalamic; L6b, layer 6b) subclasses emerge as a group highly distinct from the IT–ET-like (IT, intratelencephalic; ET, extratelencephalic) subclasses13,23,26,27, forming two distinct classes, IT–ET Glut and NP–CT–L6b Glut.”
From A high-resolution transcriptomic and spatial atlas of cell types in the whole mouse brain (2023) · cites this paper as [27]
Reference mapping to the reference snRNA-seq dataset using Azimuth validated the cell clustering results.
“To validate the cell clustering results, we performed reference mapping to the reference snRNA-seq dataset60 using the Azimuth software61 (Extended Data Fig. 5b, c).”
From Single-cell parallel analysis of DNA damage and transcriptome reveals selective genome vulnerability (2025) · cites this paper as [60]
To validate the annotation results, reference-mapping of Paired-Damage-seq RNA profile to a published snRNA-seq dataset was performed.
“To validate the annotation results, reference-mapping61 of Paired-Damage-seq RNA profile to published snRNA-seq dataset60 was performed.”
From Single-cell parallel analysis of DNA damage and transcriptome reveals selective genome vulnerability (2025) · cites this paper as [60]
The study used Patch-seq to describe the morpho-electric phenotypes for most of the t-types in MOp.
“We used the Patch-seq technique8,17–19 to describe the morpho-electric phenotypes for most of the t-types in MOp20.”
From Phenotypic variation of transcriptomic cell types in mouse motor cortex (2021) · cites this paper as [20]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cites
Fast and accurate short read alignment with Burrows-Wheeler transform
Li, H., & Durbin, R. (2009) · Bioinformatics · doi:10.1093/bioinformatics/btp324 · Cited by 58,607 · see citing works
STAR: ultrafast universal RNA-seq aligner
Dobin, A., Davis, C. A., Schlesinger, F., Drenkow, J., Zaleski, C., Jha, S., Batut, P., Chaisson, M., & Gingeras, T. R. (2013) · Bioinformatics · doi:10.1093/bioinformatics/bts635 · Cited by 51,709 · see citing works
(2008) · Journal of Machine Learning Research · Cited by 33,051 · see citing works
BEDTools: a flexible suite of utilities for comparing genomic features
Quinlan, A. R., & Hall, I. M. (2010) · Bioinformatics · doi:10.1093/bioinformatics/btq033 · Cited by 27,437 · see citing works
Fast unfolding of communities in large networks
Blondel, V. D., Guillaume, J.-L., Lambiotte, R., & Lefebvre, E. (2008) · Journal of Statistical Mechanics: Theory and Experiment · doi:10.1088/1742-5468/2008/10/p10008 · Cited by 21,424 · see citing works
Model-based Analysis of ChIP-Seq (MACS)
Zhang, Y., Liu, T., Meyer, C. A., Eeckhoute, J., Johnson, D. S., Bernstein, B. E., Nusbaum, C., Myers, R. M., Brown, M., Li, W., et al. (2008) · Genome Biology · doi:10.1186/gb-2008-9-9-r137 · Cited by 18,386 · see citing works
Comprehensive Integration of Single-Cell Data
Stuart, T., Butler, A., Hoffman, P., Hafemeister, C., Papalexi, E., Mauck, W. M., Hao, Y., Stoeckius, M., Smibert, P., & Satija, R. (2019) · Cell · doi:10.1016/j.cell.2019.05.031 · Cited by 15,453 · see citing works
Integrating single-cell transcriptomic data across different conditions, technologies, and species
Butler, A., Hoffman, P., Smibert, P., Papalexi, E., & Satija, R. (2018) · Nature Biotechnology · doi:10.1038/nbt.4096 · Cited by 13,174 · see citing works
UMAP: Uniform Manifold Approximation and Projection
McInnes, L., Healy, J., Saul, N., & Großberger, L. (2018) · Journal of Open Source Software · doi:10.21105/joss.00861 · Cited by 9,826 · see citing works
SCANPY: large-scale single-cell gene expression data analysis
Wolf, F. A., Angerer, P., & Theis, F. J. (2018) · Genome Biology · doi:10.1186/s13059-017-1382-0 · Cited by 8,881 · see citing works
A robust and high-throughput Cre reporting and characterization system for the whole mouse brain
Madisen, L., Zwingman, T. A., Sunkin, S. M., Oh, S. W., Zariwala, H. A., Gu, H., Ng, L. L., Palmiter, R. D., Hawrylycz, M. J., Jones, A. R., et al. (2010) · Nature Neuroscience · doi:10.1038/nn.2467 · Cited by 6,907 · see citing works
Self-citationFrom Louvain to Leiden: guaranteeing well-connected communities
Traag, V. A., Waltman, L., & van Eck, N. J. (2019) · Scientific Reports · doi:10.1038/s41598-019-41695-z · Cited by 5,635 · see citing works
Genome-wide atlas of gene expression in the adult mouse brain
Lein, E. S., Hawrylycz, M. J., Ao, N., Ayres, M., Bensinger, A., Bernard, A., Boe, A. F., Boguski, M. S., Brockway, K. S., Byrnes, E. J., et al. (2007) · Nature · doi:10.1038/nature05453 · Cited by 5,572 · see citing works
Self-citationBismark: a flexible aligner and methylation caller for Bisulfite-Seq applications
Krueger, F., & Andrews, S. (2011) · Bioinformatics · doi:10.1093/bioinformatics/btr167 · Cited by 5,161 · see citing works
McGinnis, C. S., Murrow, L. M., & Gartner, Z. J. (2019) · Cell Systems · doi:10.1016/j.cels.2019.03.003 · Cited by 4,521 · see citing works
Software for Computing and Annotating Genomic Ranges
Lawrence, M. F., Huber, W. K., Pagès, H., Aboyoun, P., Carlson, M. R. J., Gentleman, R. C., Morgan, M. T., & Carey, V. J. (2013) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1003118 · Cited by 4,438 · see citing works
FIMO: scanning for occurrences of a given motif
Grant, C. E., Bailey, T. L., & Noble, W. S. (2011) · Bioinformatics · doi:10.1093/bioinformatics/btr064 · Cited by 4,247 · see citing works
Molecular Architecture of the Mouse Nervous System
Zeisel, A., Hochgerner, H., Lönnerberg, P., Johnsson, A., Memic, F., van der Zwan, J., Häring, M., Braun, E., Borm, L. E., La Manno, G., et al. (2018) · Cell · doi:10.1016/j.cell.2018.06.021 · Cited by 2,995 · see citing works
Scrublet: computational identification of cell doublets in single-cell transcriptomic data
Wolock, S. L., Lopez, R., & Klein, A. M. (2019) · Cell Systems · doi:10.1016/j.cels.2018.11.005 · Cited by 2,758 · see citing works
Scrublet: computational identification of cell doublets in single-cell transcriptomic data
Wolock, S. L., Lopez, R., & Klein, A. M. (2018) · doi:10.1101/357368 · Cited by 2,758 · see citing works
Batch effects in single-cell RNA-sequencing data are corrected by matching mutual nearest neighbors
Haghverdi, L., Lun, A. T. L., Morgan, M. D., & Marioni, J. C. (2018) · Nature Biotechnology · doi:10.1038/nbt.4091 · Cited by 2,416 · see citing works
Tripathi, V., Ellis, J. D., Shen, Z., Song, D. Y., Pan, Q., Watt, A. T., Freier, S. M., Bennett, C. F., Sharma, A., Bubulya, P. A., et al. (2010) · Molecular Cell · doi:10.1016/j.molcel.2010.08.011 · Cited by 2,223 · see citing works
Single-cell mRNA quantification and differential analysis with Census
Qiu, X., Hill, A. J., Packer, J. S., Lin, D., Ma, Y.-A., & Trapnell, C. (2017) · Nature Methods · doi:10.1038/nmeth.4150 · Cited by 1,885 · see citing works
Molecular diversity and specializations among the cells of the adult mouse brain
Saunders, A., Macosko, E. Z., Wysoker, A., Goldman, M., Krienen, F. M., de Rivera, H., Bien, E., Baum, M., Bortolin, L., Wang, S., et al. (2018) · Cell · doi:10.1016/j.cell.2018.07.028 · Cited by 1,847 · see citing works
Self-citationThe ENCODE Blacklist: Identification of Problematic Regions of the Genome
Amemiya, H. M., Kundaje, A., & Boyle, A. P. (2019) · Scientific Reports · doi:10.1038/s41598-019-45839-z · Cited by 1,831 · see citing works
Global epigenomic reconfiguration during mammalian brain development
Lister, R., Mukamel, E. A., Nery, J. R., Urich, M., Puddifoot, C. A., Johnson, N. D., Lucero, J., Huang, Y., Dwork, A. J., Schultz, M. D., et al. (2013) · Science · doi:10.1126/science.1237905 · Cited by 1,830 · see citing works
Self-citationRecovering Gene Interactions from Single-Cell Data Using Data Diffusion
van Dijk, D., Sharma, R., Nainys, J., Yim, K., Kathail, P., Carr, A. J., Burdziak, C., Moon, K. R., Chaffer, C. L., Pattabiraman, D., et al. (2018) · Cell · doi:10.1016/j.cell.2018.05.061 · Cited by 1,829 · see citing works
Conserved cell types with divergent features in human versus mouse cortex
Hodge, R. D., Bakken, T. E., Miller, J. A., Smith, K. A., Barkan, E. R., Graybuck, L. T., Close, J. L., Long, B., Johansen, N., Penn, O., et al. (2019) · Nature · doi:10.1038/s41586-019-1506-7 · Cited by 1,775 · see citing works
Self-citationJASPAR 2020: update of the open-access database of transcription factor binding profiles
Fornes, O., Castro-Mondragon, J. A., Khan, A., van der Lee, R., Zhang, X., Richmond, P. A., Modi, B. P., Correard, S., Gheorghe, M., Baranašić, D., et al. (2019) · Nucleic Acids Research · doi:10.1093/nar/gkz1001 · Cited by 1,763 · see citing works
Shared and distinct transcriptomic cell types across neocortical areas
Tasic, B., Yao, Z., Graybuck, L. T., Smith, K. A., Nguyen, T. N., Bertagnolli, D., Goldy, J., Garren, E., Economo, M. N., Viswanathan, S., et al. (2018) · Nature · doi:10.1038/s41586-018-0654-5 · Cited by 1,746 · see citing works
Self-citationAdult mouse cortical cell taxonomy revealed by single cell transcriptomics
Tasic, B., Menon, V., Nguyen, T. N., Kim, T. K., Jarsky, T., Yao, Z., Levi, B., Gray, L. T., Sorensen, S. A., Dolbeare, T., et al. (2016) · Nature Neuroscience · doi:10.1038/nn.4216 · Cited by 1,714 · see citing works
Self-citationSC3: consensus clustering of single-cell RNA-seq data
Kiselev, V. Y., Kirschner, K., Schaub, M. T., Andrews, T., Yiu, A., Chandra, T., Natarajan, K. N., Reik, W., Barahona, M., Green, A. R., et al. (2017) · Nature Methods · doi:10.1038/nmeth.4236 · Cited by 1,500 · see citing works
Impact of cytosine methylation on DNA binding specificities of human transcription factors
Yin, Y., Morgunova, E., Jolma, A., Kaasinen, E., Sahu, B., Khund-Sayeed, S., Das, P. K., Kivioja, T., Dave, K., Zhong, F., et al. (2017) · Science · doi:10.1126/science.aaj2239 · Cited by 1,257 · see citing works
The chromatin accessibility landscape of primary human cancers
Corces, M. R., Granja, J. M., Shams, S., Louie, B. H., Seoane, J. A., Zhou, W., Silva, T. C., Groeneveld, C., Wong, C. K., Cho, S. W., et al. (2018) · Science · doi:10.1126/science.aav1898 · Cited by 1,230 · see citing works
Single-Cell Multi-omic Integration Compares and Contrasts Features of Brain Cell Identity
Welch, J. D., Kozareva, V., Ferreira, A., Vanderburg, C., Martin, C., & Macosko, E. Z. (2019) · Cell · doi:10.1016/j.cell.2019.05.006 · Cited by 1,193 · see citing works
Self-citationSatpathy, A. T., Granja, J. M., Yost, K. E., Qi, Y., Meschi, F., McDermott, G. P., Olsen, B. N., Mumbach, M. R., Pierce, S. E., Corces, M. R., et al. (2019) · Nature Biotechnology · doi:10.1038/s41587-019-0206-z · Cited by 980 · see citing works
Joint profiling of chromatin accessibility and gene expression in thousands of single cells
Cao, J., Cusanovich, D. A., Ramani, V., Aghamirzaie, D., Pliner, H. A., Hill, A. J., Daza, R. M., McFaline-Figueroa, J. L., Packer, J. S., Christiansen, L., et al. (2018) · Science · doi:10.1126/science.aau0730 · Cited by 938 · see citing works
Neuronal cell-type classification: challenges, opportunities and the path forward
Zeng, H., & Sanes, J. R. (2017) · Nature Reviews Neuroscience · doi:10.1038/nrn.2017.85 · Cited by 841 · see citing works
Self-citationEpigenomic Analysis of Multilineage Differentiation of Human Embryonic Stem Cells
Xie, W., Schultz, M. D., Lister, R., Hou, Z., Rajagopal, N., Ray, P. R., Whitaker, J. W., Tian, S., Hawkins, R. D., Leung, D., et al. (2013) · Cell · doi:10.1016/j.cell.2013.04.022 · Cited by 721 · see citing works
Self-citationHuman body epigenome maps reveal noncanonical DNA methylation variation
Schultz, M. D., He, Y., Whitaker, J. W., Hariharan, M., Mukamel, E. A., Leung, D., Rajagopal, N., Nery, J. R., Urich, M. A., Chen, H., et al. (2015) · Nature · doi:10.1038/nature14465 · Cited by 690 · see citing works
Self-citationPurhonen, J., Banerjee, R., McDonald, A. E., Fellman, V., & Kallijärvi, J. (2020) · Nucleic Acids Research · doi:10.1093/nar/gkaa516 · Cited by 657 · see citing works
Single-nucleus and single-cell transcriptomes compared in matched cortical cell types
Bakken, T. E., Hodge, R. D., Miller, J. A., Yao, Z., Nguyen, T. N., Aevermann, B., Barkan, E., Bertagnolli, D., Casper, T., Dee, N., et al. (2018) · PLoS ONE · doi:10.1371/journal.pone.0209648 · Cited by 608 · see citing works
Self-citationSingle-cell methylomes identify neuronal subtypes and regulatory elements in mammalian cortex
Luo, C., Keown, C. L., Kurihara, L., Zhou, J., He, Y., Li, J., Castanon, R., Lucero, J., Nery, J. R., Sandoval, J. P., et al. (2017) · Science · doi:10.1126/science.aan3351 · Cited by 545 · see citing works
Self-citationSpatially resolved cell atlas of the mouse primary motor cortex by MERFISH
Zhang, M., Eichhorn, S. W., Zingg, B., Yao, Z., Cotter, K., Zeng, H., Dong, H., & Zhuang, X. (2021) · Nature · doi:10.1038/s41586-021-03705-x · Cited by 498 · see citing works
Self-citationAnatomical characterization of Cre driver mice for neural circuit mapping and manipulation
Harris, J. A., Hirokawa, K. E., Sorensen, S. A., Gu, H., Mills, M., Ng, L. L., Bohn, P., Mortrud, M., Ouellette, B., Kidney, J., et al. (2014) · Frontiers in Neural Circuits · doi:10.3389/fncir.2014.00076 · Cited by 485 · see citing works
Self-citationEpigenetic memory at embryonic enhancers identified in DNA methylation maps from adult mouse tissues
Hon, G. C., Rajagopal, N., Shen, Y., McCleary, D. F., Yue, F., Dang, M. D., & Ren, B. (2013) · Nature Genetics · doi:10.1038/ng.2746 · Cited by 457 · see citing works
Self-citationComprehensive analysis of single cell ATAC-seq data with SnapATAC
Fang, R., Preissl, S., Li, Y., Hou, X., Lucero, J., Wang, X., Motamedi, A., Shiau, A. K., Zhou, X., Xie, F., et al. (2021) · Nature Communications · doi:10.1038/s41467-021-21583-9 · Cited by 455 · see citing works
Self-citationTranscriptional Architecture of Synaptic Communication Delineates GABAergic Neuron Identity
Paul, A., Crow, M., Raudales, R., He, M., Gillis, J., & Huang, Z. J. (2017) · Cell · doi:10.1016/j.cell.2017.08.032 · Cited by 421 · see citing works
Self-citationDistinct descending motor cortex pathways and their roles in movement
Economo, M. N., Viswanathan, S., Tasic, B., Bas, E., Winnubst, J., Menon, V., Graybuck, L. T., Nguyen, T. N., Smith, K. A., Yao, Z., et al. (2018) · Nature · doi:10.1038/s41586-018-0642-9 · Cited by 415 · see citing works
Self-citationNuclear RNA-seq of single neurons reveals molecular signatures of activation
Lacar, B., Linker, S. B., Jaeger, B. N., Krishnaswami, S. R., Barron, J. J., Kelder, M. J. E., Parylak, S. L., Paquola, A. C. M., Venepally, P., Novotny, M., et al. (2016) · Nature Communications · doi:10.1038/ncomms11022 · Cited by 405 · see citing works
Single-cell multimodal omics: the power of many
Zhu, C., Preissl, S., & Ren, B. (2020) · Nature Methods · doi:10.1038/s41592-019-0691-5 · Cited by 386 · see citing works
Self-citationTushev, G., Glock, C., Heumüller, M., Biever, A., Jovanovic, M., & Schuman, E. M. (2018) · Neuron · doi:10.1016/j.neuron.2018.03.030 · Cited by 377 · see citing works
Crow, M., Paul, A., Ballouz, S., Huang, Z. J., & Gillis, J. (2018) · Nature Communications · doi:10.1038/s41467-018-03282-0 · Cited by 365 · see citing works
Self-citationPreissl, S., Fang, R., Huang, H., Zhao, Y., Raviram, R., Gorkin, D. U., Zhang, Y., Sos, B. C., Afzal, V., Dickel, D. E., et al. (2018) · Nature Neuroscience · doi:10.1038/s41593-018-0079-3 · Cited by 332 · see citing works
Self-citationClasses and continua of hippocampal CA1 inhibitory neurons revealed by single-cell transcriptomics
Harris, K. D., Hochgerner, H., Skene, N. G., Magno, L., Katona, L., Bengtsson Gonzales, C., Somogyi, P., Kessaris, N., Linnarsson, S., & Hjerling-Leffler, J. (2018) · PLoS Biology · doi:10.1371/journal.pbio.2006387 · Cited by 276 · see citing works
Single-Cell Sequencing of Brain Cell Transcriptomes and Epigenomes
Armand, E. J., Li, J., Xie, F., Luo, C., & Mukamel, E. A. (2021) · Neuron · doi:10.1016/j.neuron.2020.12.010 · Cited by 241 · see citing works
Self-citationRobust single-cell DNA methylome profiling with snmC-seq2
Luo, C., Rivkin, A., Zhou, J., Sandoval, J. P., Kurihara, L., Lucero, J., Castanon, R., Nery, J. R., Pinto-Duarte, A., Bui, B., et al. (2018) · Nature Communications · doi:10.1038/s41467-018-06355-2 · Cited by 189 · see citing works
Self-citationTFClass: an expandable hierarchical classification of human transcription factors
Wingender, E., Schoeps, T., & Dönitz, J. (2012) · Nucleic Acids Research · doi:10.1093/nar/gks1123 · Cited by 164 · see citing works
Spatiotemporal DNA methylome dynamics of the developing mouse fetus
He, Y., Hariharan, M., Gorkin, D. U., Dickel, D. E., Luo, C., Castanon, R., Nery, J. R., Lee, A. Y., Zhao, Y., Huang, H., et al. (2020) · Nature · doi:10.1038/s41586-020-2119-x · Cited by 161 · see citing works
Self-citationA genuine layer 4 in motor cortex with prototypical synaptic circuit connectivity
Yamawaki, N., Borges, K., Suter, B. A., Harris, K. D., & Shepherd, G. M. G. (2014) · eLife · doi:10.7554/elife.05422 · Cited by 141 · see citing works
Peukert, D., Weber, S., Lumsden, A., & Scholpp, S. (2011) · PLoS Biology · doi:10.1371/journal.pbio.1001218 · Cited by 118 · see citing works
RORβ Induces Barrel-like Neuronal Clusters in the Developing Neocortex
Jabaudon, D., Shnider, S. J., Tischfield, D. J., Galazo, M. J., & Macklis, J. D. (2011) · Cerebral Cortex · doi:10.1093/cercor/bhr182 · Cited by 100 · see citing works
Common cell type nomenclature for the mammalian brain
Miller, J. A., Gouwens, N. W., Tasic, B., Collman, F., van Velthoven, C. T. J., Bakken, T. E., Hawrylycz, M., Zeng, H., Lein, E. S., & Bernard, A. (2020) · eLife · doi:10.7554/elife.59928 · Cited by 73 · see citing works
Self-citationVruwink, M., Schmidt, H. H. H. W., Weinberg, R. J., & Burette, A. (2001) · The Journal of Comparative Neurology · doi:10.1002/cne.1413 · Cited by 72 · see citing works
Improved regulatory element prediction based on tissue-specific local epigenomic signatures
He, Y., Gorkin, D. U., Dickel, D. E., Nery, J. R., Castanon, R., Lee, A. Y., Shen, Y., Visel, A., Pennacchio, L. A., Ren, B., et al. (2017) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1618353114 · Cited by 70 · see citing works
Self-citationPerspectives on defining cell types in the brain
Mukamel, E. A., & Ngai, J. (2019) · Current Opinion in Neurobiology · doi:10.1016/j.conb.2018.11.007 · Cited by 56 · see citing works
Self-citationLuo, C., Liu, H., Xie, F.-M., et al. (2019) · bioRxiv · doi:10.1101/2019.12.11.873398 · Cited by 27 · see citing works
Self-citationWiring together large single-cell RNA-seq sample collectionsPreprint
Barkas, N., Petukhov, V., Nikolaeva, D., et al. (2018) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/460246 · Cited by 16 · see citing works
Substance P signalling in primary motor cortex facilitates motor learning in rats
Hertler, B., Hosp, J. A., Blanco, M. B., et al. (2017) · PLoS ONE · doi:10.1371/journal.pone.0189812 · Cited by 12 · see citing works
Frozen Tissue Nuclei Extraction (for 10xV3 snSEQ) v2Preprint
Martin, C., Abdul, A., Vanderburg, C. R., et al. (2020) · doi:10.17504/protocols.io.bi62khge · Cited by 8 · see citing works
Self-citationFrozen Tissue Nuclei Extraction (for 10xV3 snSEQ) v1Preprint
Martin, C., Abdul, A., Vanderburg, C. R., et al. (2020) · doi:10.17504/protocols.io.bck6iuze · Cited by 7 · see citing works
Self-citationFresh Frozen Mouse Brain Preparation (for Single Nuclei Sequencing) v1Preprint
Vanderburg, C., Martin, C. A., Kozareva, V., et al. (2020) · doi:10.17504/protocols.io.bcbrism6 · Cited by 5 · see citing works
Self-citationExtraction of Nuclei from Brain Tissue v1Preprint
Bortolin, L., Goldman, M., & McCarroll, S. A. (2019) · doi:10.17504/protocols.io.2srged6 · Cited by 4 · see citing works
Methyl-C sequencing of single cell nuclei: snmC-seq2Preprint
Luo, C. (2018) · doi:10.17504/protocols.io.pjvdkn6 · Cited by 2 · see citing works
Self-citationSequencing open chromatin of single cell nuclei: snATAC-seqPreprint
Preissl, S., & Wang, X. (2018) · doi:10.17504/protocols.io.pjudknw · Cited by 2 · see citing works
Self-citationSequencing open chromatin of single cell nuclei: snATAC-seq v2Preprint
Preißl, S., Wang, X., & Ren, B. (2019) · doi:10.17504/protocols.io.4zzgx76 · Cited by 1 · see citing works
Self-citation
Flagged first, then by citations
Research works printed but not linked: 2
- [13]Waddington, C. H. The Strategy of the Genes (Routledge, 1957).
- [19]Brodmann, K. Brodmann’s: Localisation in the Cerebral Cortex (Springer Science & Business Media, 2007).
Cited by
Sorted by citations
Spatiotemporal transcriptomic atlas of mouse organogenesis using DNA nanoball-patterned arrays
Chen, A., Liao, S., Cheng, M., Ma, K., Wu, L., Lai, Y., Qiu, X., Yang, J., Xu, J., Hao, S., et al. (2022) · Cell · doi:10.1016/j.cell.2022.04.003 · Cited by 1,784 · see citing works
Self-citationA high-resolution transcriptomic and spatial atlas of cell types in the whole mouse brain
Yao, Z., van Velthoven, C. T. J., Kunst, M., Zhang, M., McMillen, D., Lee, C., Jung, W., Goldy, J., Abdelhak, A., Aitken, M., et al. (2023) · Nature · doi:10.1038/s41586-023-06812-z · Cited by 1,194 · see citing works
Self-citationComparative cellular analysis of motor cortex in human, marmoset and mouse
Bakken, T. E., Jorstad, N. L., Hu, Q., Lake, B. B., Tian, W., Kalmbach, B. E., Crow, M., Hodge, R. D., Krienen, F. M., Sorensen, S. A., et al. (2021) · Nature · doi:10.1038/s41586-021-03465-8 · Cited by 812 · see citing works
Self-citationMulti-omics single-cell data integration and regulatory inference with graph-linked embedding
Cao, Z.-J., & Gao, G. (2022) · Nature Biotechnology · doi:10.1038/s41587-022-01284-4 · Cited by 616 · see citing works
Spatially resolved cell atlas of the mouse primary motor cortex by MERFISH
Zhang, M., Eichhorn, S. W., Zingg, B., Yao, Z., Cotter, K., Zeng, H., Dong, H., & Zhuang, X. (2021) · Nature · doi:10.1038/s41586-021-03705-x · Cited by 498 · see citing works
Self-citationWhat is a cell type and how to define it?
Zeng, H. (2022) · Cell · doi:10.1016/j.cell.2022.06.031 · Cited by 439 · see citing works
Self-citationMarsh, S. E., Walker, A. J., Kamath, T., Dissing-Olesen, L., Hammond, T. R., de Soysa, T. Y., Young, A. M. H., Murphy, S., Abdulraouf, A., Nadaf, N., et al. (2022) · Nature Neuroscience · doi:10.1038/s41593-022-01022-8 · Cited by 432 · see citing works
Self-citationPhenotypic variation of transcriptomic cell types in mouse motor cortex
Scala, F., Kobak, D., Bernabucci, M., Bernaerts, Y., Cadwell, C. R., Castro, J. R., Hartmanis, L., Jiang, X., Laturnus, S., Miranda, E., et al. (2021) · Nature · doi:10.1038/s41586-020-2907-3 · Cited by 398 · see citing works
Self-citationDNA methylation atlas of the mouse brain at single-cell resolution
Liu, H., Zhou, J., Tian, W., Luo, C., Bartlett, A., Aldridge, A., Lucero, J., Osteen, J. K., Nery, J. R., Chen, H., et al. (2021) · Nature · doi:10.1038/s41586-020-03182-8 · Cited by 289 · see citing works
Self-citationHypoMap—a unified single-cell gene expression atlas of the murine hypothalamus
Steuernagel, L., Lam, B. Y. H., Klemm, P., Dowsett, G. K. C., Bauder, C. A., Tadross, J. A., Hitschfeld, T. S., del Rio Martin, A., Chen, W., de Solis, A. J., et al. (2022) · Nature Metabolism · doi:10.1038/s42255-022-00657-y · Cited by 267 · see citing works
Single-Cell Sequencing of Brain Cell Transcriptomes and Epigenomes
Armand, E. J., Li, J., Xie, F., Luo, C., & Mukamel, E. A. (2021) · Neuron · doi:10.1016/j.neuron.2020.12.010 · Cited by 241 · see citing works
Self-citationSingle-cell spatial transcriptome reveals cell-type organization in the macaque cortex
Chen, A., Sun, Y., Lei, Y., Li, C., Liao, S., Meng, J., Bai, Y., Liu, Z., Liang, Z., Zhu, Z., et al. (2023) · Cell · doi:10.1016/j.cell.2023.06.009 · Cited by 232 · see citing works
Self-citationEarly Alzheimer’s disease pathology in human cortex involves transient cell states
Gazestani, V., Kamath, T., Nadaf, N. M., Dougalis, A., Burris, S. J., Rooney, B., Junkkari, A., Vanderburg, C., Pelkonen, A., Gomez-Budia, M., et al. (2023) · Cell · doi:10.1016/j.cell.2023.08.005 · Cited by 209 · see citing works
Self-citationAn atlas of gene regulatory elements in adult mouse cerebrum
Li, Y. E., Preissl, S., Hou, X., Zhang, Z., Zhang, K., Qiu, Y., Poirion, O. B., Li, B., Chiou, J., Liu, H., et al. (2021) · Nature · doi:10.1038/s41586-021-03604-1 · Cited by 185 · see citing works
Self-citationA comparative atlas of single-cell chromatin accessibility in the human brain
Li, Y. E., Preissl, S., Miller, M., Johnson, N. D., Wang, Z., Jiao, H., Zhu, C., Wang, Z., Xie, Y., Poirion, O., et al. (2023) · Science · doi:10.1126/science.adf7044 · Cited by 173 · see citing works
Self-citationCobolt: integrative analysis of multimodal single-cell sequencing data
Gong, B., Zhou, Y., & Purdom, E. (2021) · Genome Biology · doi:10.1186/s13059-021-02556-z · Cited by 168 · see citing works
Self-citationNicheformer: a foundation model for single-cell and spatial omics
Tejada-Lapuerta, A., Schaar, A. C., Gutgesell, R., Palla, G., Halle, L., Minaeva, M., Vornholz, L., Dony, L., Drummer, F., Richter, T., et al. (2025) · Nature Methods · doi:10.1038/s41592-025-02814-z · Cited by 100 · see citing works
Droplet-based single-cell joint profiling of histone modifications and transcriptomes
Xie, Y., Zhu, C., Wang, Z., Tastemel, M., Chang, L., Li, Y. E., & Ren, B. (2023) · Nature Structural & Molecular Biology · doi:10.1038/s41594-023-01060-1 · Cited by 69 · see citing works
Self-citationDuttke, S. H., Montilla-Perez, P., Chang, M. W., Li, H., Chen, H., Carrette, L. L. G., Guglielmo, G. D., George, O., Palmer, A. A., Benner, C., et al. (2022) · Frontiers in Neuroscience · doi:10.3389/fnins.2022.858427 · Cited by 26 · see citing works
Single-cell parallel analysis of DNA damage and transcriptome reveals selective genome vulnerability
Bai, D., Cao, Z., Attada, N., Song, J., & Zhu, C. (2025) · Nature Methods · doi:10.1038/s41592-025-02632-3 · Cited by 16 · see citing works
- Authors
- Yao, Z., Liu, H., Xie, F., Fischer, S., Adkins, R. S., Aldridge, A. I., Ament, S. A., Bartlett, A., Behrens, M. M., Van den Berge, K., Bertagnolli, D., de Bézieux, H. R., Biancalani, T., Booeshaghi, A. S., Bravo, H. C., Casper, T., Colantuoni, C., Crabtree, J., Creasy, H., Crichton, K., Crow, M., Dee, N., Dougherty, E. L., Doyle, W. I., Dudoit, S., Fang, R., Felix, V., Fong, O., Giglio, M., Goldy, J., Hawrylycz, M., Herb, B. R., Hertzano, R., Hou, X., Hu, Q., Kancherla, J., Kroll, M., Lathia, K., Li, Y. E., Lucero, J. D., Luo, C., Mahurkar, A., McMillen, D., Nadaf, N. M., Nery, J. R., Nguyen, T. N., Niu, S.-Y., Ntranos, V., Orvis, J., Osteen, J. K., Pham, T., Pinto-Duarte, A., Poirion, O., Preissl, S., Purdom, E., Rimorin, C., Risso, D., Rivkin, A. C., Smith, K., Street, K., Sulc, J., Svensson, V., Tieu, M., Torkelson, A., Tung, H., Vaishnav, E. D., Vanderburg, C. R., van Velthoven, C., Wang, X., White, O. R., Huang, Z. J., Kharchenko, P. V., Pachter, L., Ngai, J., Regev, A., Tasic, B., Welch, J. D., Gillis, J., Macosko, E. Z., Ren, B., Ecker, J. R., Zeng, H., & Mukamel, E. A.
- Affiliations
- Allen Institute for Brain Science (US)
- Salk Institute for Biological Studies (US)
- University of California San Diego (US)
- Cold Spring Harbor Laboratory (US)
- University of Maryland, Baltimore (US)
- Broad Institute (US)
- California Institute of Technology (US)
- University of Maryland, College Park (US)
- University of California, Berkeley (US)
- Harvard University (US)
- Ludwig Cancer Research (US)
- University of California, San Francisco (US)
- University of Padua (IT)
- Dana-Farber Cancer Institute (US)
- Washington University in St. Louis (US)
- University of Michigan (US)
- Corresponding author
- Zeng, Hongkui
- Mukamel, Eran A.
- ORCID
- Yao, Zizhen
- Liu, Hanqing
- Xie, Fangming
- Fischer, Stephan
- Adkins, Ricky S.
- Aldridge, Andrew I.
- Ament, Seth A.
- Bartlett, Anna
- Behrens, M. Margarita
- Bertagnolli, Darren
- de Bézieux, Hector Roux
- Biancalani, Tommaso
- Booeshaghi, A. Sina
- Bravo, Héctor Corrada
- Casper, Tamara
- Colantuoni, Carlo
- Crabtree, Jonathan
- Creasy, Heather
- Crichton, Kirsten
- Crow, Megan
- Dee, Nick
- Dougherty, Elizabeth L.
- Doyle, Wayne I.
- Dudoit, Sandrine
- Fang, Rongxin
- Felix, Victor
- Fong, Olivia
- Giglio, Michelle
- Goldy, Jeff
- Hawrylycz, Mike
- Herb, Brian R.
- Hertzano, Ronna
- Hou, Xiaomeng
- Hu, Qiwen
- Kancherla, Jayaram
- Kroll, Matthew
- Lathia, Kanan
- Li, Yang Eric
- Lucero, Jacinta D.
- Luo, Chongyuan
- Mahurkar, Anup
- McMillen, Delissa
- Nadaf, Naeem M.
- Nery, Joseph R.
- Nguyen, Thuc Nghi
- Niu, Sheng-Yong
- Ntranos, Vasilis
- Orvis, Joshua
- Osteen, Julia K.
- Pham, Thanh
- Pinto-Duarte, Antonio
- Poirion, Olivier
- Preissl, Sebastian
- Purdom, Elizabeth
- Rimorin, Christine
- Risso, Davide
- Rivkin, Angeline C.
- Smith, Kimberly
- Street, Kelly
- Svensson, Valentine
- Tieu, Michael
- Torkelson, Amy
- Tung, Herman
- Vaishnav, Eeshit Dhaval
- Vanderburg, Charles R.
- van Velthoven, Cindy
- Wang, Xinxin
- White, Owen R.
- Huang, Z. Josh
- Kharchenko, Peter V.
- Pachter, Lior
- Ngai, John
- Regev, Aviv
- Tasic, Bosiljka
- Welch, Joshua D.
- Gillis, Jesse
- Macosko, Evan Z.
- Ren, Bing
- Ecker, Joseph R.
- Zeng, Hongkui
- Mukamel, Eran A.
- Venue
- Nature
- Published
- 2021-10-06
- Volume
- 598
- Issue
- 7879
- Pages
- 103-110
- ISSN
- 0028-0836 (ISSN-L)
- 1476-4687
- PMC
- PMC8494649
- OpenAlex
- W3202498768
- Cited by
- 398
- References
- 76
- Citations by year
- 2026: 75 · 2025: 79 · 2024: 70 · 2023: 69 · 2022: 54 · 2021: 38 · 2020: 13
- FWCI
- 19.05
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Locations
- Nature (publishedVersion, cc-by)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by-nc-nd)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Cold Spring Harbor Laboratory Institutional Repository (Cold Spring Harbor Laboratory) (acceptedVersion)
- CaltechAUTHORS (California Institute of Technology) (submittedVersion, cc-by-nc-nd)
- Digital Commons@Becker (Washington University School of Medicine) (submittedVersion, cc-by)
- RePEc: Research Papers in Economics (submittedVersion)
- Ghent University Academic Bibliography (Ghent University) (submittedVersion, cc-by)
- DSpace@MIT (Massachusetts Institute of Technology) (submittedVersion, cc-by-nc)
- eScholarship (California Digital Library) (submittedVersion)
- eScholarship (California Digital Library) (submittedVersion, cc-by)
- FreiDok plus (Universitätsbibliothek Freiburg) (submittedVersion)
- Padua Research Archive (University of Padova) (submittedVersion, cc-by)
- Funders
- Hearing Health Foundation
- National Institutes of Health (DC013817)
- National Institute of Mental Health (U24MH114827, U19MH114821, RF1MH123199, U01MH121282, R24MH114815, U19MH114830, R24MH114788)
- National Cancer Institute (P30CA014195)
- National Institute on Deafness and Other Communication Disorders (DC013817, R01DC019370, R01DC013817, U01DC013817)
- National Institute of General Medical Sciences (GM114267, R01GM114267)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Single-cell and spatial transcriptomics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Genomics and Chromatin Dynamics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene Regulatory Network Analysis
- Keywords
- primary motor cortex, cell atlas, single-cell transcriptomics, epigenomics, neuronal cell types, multimodal data integration, marker genes, gene regulatory elements, pyramidal neurons, mouse brain
- MeSH terms
- Animals, Atlases as Topic, Female, Male, Motor Cortex, Neurons, Organ Specificity, Reproducibility of Results, Gene Expression Profiling, Epigenesis, Genetic, Mice, Epigenomics, Single-Cell Analysis, Transcriptome, Datasets as Topic
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
